Research Journal of Applied Sciences, Engineering and Technology 7(15): 3174-3180, 2014
ISSN: 2040-7459; e-ISSN: 2040-7467
© Maxwell Scientific Organization, 2014
Submitted: October 26, 2013
Accepted: November 08, 2013
Published: April 19, 2014
Using Frequency Ratio Method for Spatial Landslide Prediction
1
Mirnazari, Javad, 1Ahmad, Baharin, 2Mojaradi, Barat and 1Sattari, Farshid
Department of Geoinformation, Faculty of Geoinformation and Real Estate,
UTM University Malaysia, Malaysia
2
School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
1
Abstract: Numerous landslides have occurred in the study area and they damage to agriculture and pasturelands.
Since the study area do not have any landslide inventory and landslide predicted maps, landslide inventory produced
based on field research (GPS) and satellite image (Geoeye and Ikonos). Frequency ratio technique is a statistical
approach to simulation environmental conditions. It also uses to take the factors related to dependent variable.
Frequency technique considered for generating landslide susceptibility map. Pixel landsliding and non-landsliding
calculated in eight factors related-landslide. Landslide susceptibility map produce in five insensitive to very high
sensitive classes based on natural breaks method. Receiver Operating Characteristics (ROC) graph implement to
evaluate of the frequency ratio method. In particular, the model will be able to predict landslide area occurrence in
future completely (sensitivity = 1). Although, model identify insensitive area with 17% errors (specificity = 0.83).
Keywords: Frequency ratio, landslide, ROC curve, validation
INTRODUCTION
Landslide is the world’s third largest natural
disaster that causes many damages (Zillman, 2000).
Human casualties and settlement damages as well as the
infrastructure problems caused by landslide are
increasing worldwide. In the world scale, landslides
cause billions of dollars in loss and thousands of deaths
and injuries each year. Most part of Iran has mass
movement, which includes landslide, rock fall, earth
flow and creep because of relief and mountainous
terrain. The Study area in this research is west of Iranan area of 250 km2 in the Kermanshah province in Iran.
This area is quite susceptible to landslide due to its
climatic
conditions,
geology,
geomorphologic
characteristics and human activities. Many villages and
farms are located on unstable ground and then if
landslide susceptibility map is prepared, it can help to
displace some buildings in hazardous area. Landslide
hazard evaluation is based on the analysis of the ground
conditions in those regions where a previous landslide
occurred (Carrara et al., 1999). The purpose of the
present study was to produce landslide inventory and
then landslide susceptibility map of the selected area by
frequency ratio approach. Statistical methods are more
applicable for prediction and classification of
environmental problems in various regions (Paliwal and
Kumar, 2009). Landslide zonation mapping methods
can be divided into the following categories:
quantitative or statistical (Guzzetti et al., 1999; Rautela
P
P
and Lakhera, 2000; Lineback et al., 2001; Cevik and
Topal, 2003; Gorsevski et al., 2003; Lee, 2004;
Tangestani, 2004; Sakellariou and Ferentinou, 2005;
Ayalew and Yamagishi, 2005), deterministic methods
(Gökceoglu and Aksoy, 1996) and qualitative or
knowledge-based (Ives and Messerli, 1981; Rupke and
Veilleux, 2011; Regmi et al., 2010).
For the landslide-hazard analysis, the main steps
were data collection and construction of a spatial
database from which the relevant factors were
extracted, followed by assessment of the landslide
hazard using the relation between the landslide and
landslide-related factors and validation of the results. A
key assumption of this approach is that the past and
present is the key to the future. In other words, the
potential (occurrence possibility) of landslides can be
comparable to the actual frequency of landslides
(Pradhan and Lee, 2010).
The aim of present article is to use and to identify
the implications frequency ratio method for generating
sensitive land in the study area for landsliding. It also to
evaluate the accuracy of the model in ROC graph and to
present advantage and disadvantage of the frequency
ratio method.
Study area: The study area lies in the east south of
Kermanshah province in west of Iran. It is mountainous
area between 34°:05':6"N to 34°:13':06"N latitude and
47°:22':03" E to 47°:35':26"E longitude, with a total
area of 250 km² (Fig. 1). It is characterized by rugged
Corresponding Author: Ahmad, Baharin, Department of Geoinformation, Faculty of Geoinformation and Real Estate, UTM
University Malaysia, Malaysia, Tel.: +6075530951
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Res. J. App. Sci. Eng. Technol., 7(15): 3174-3180, 2014
hills and mountain terrains covered by scattered trees
and forest fragmentation. The study area is frequently
subjected to landslides following land use change,
especially alongside the agriculture land since they
were changed (Fig. 1 to 3).
MATERIALS AND METHODS
Fig. 2: Effect of land-use on shallow landslide
Landslide inventory: Landslide inventory maps show
locations and characteristics of landslides that have
displaced in the past but generally do not indicate the
mechanism (s) that triggered them. Landslide inventory
map necessary to evaluate the validation in any study
for assessing or zoning of landslide. After
implementation of our method, we need to evaluate the
model, so only landslides previously occurred could
help to assess of the result. Therefore, inventory maps
provide useful information about the landslide potential
area. In addition, recognizing the type and recency of
land-sliding can also facilitate the scope and design of
site-specific geotechnical investigations and guide for
slope remediation strategies. In previous studies, the
inventory maps will be prepared from four methods that
including: geo-morphological, event, seasonal and
multi- temporal inventories (Guzzetti et al., 2012). For
the study area we determined some situation landslides
(about 35% of total exist landslides in area (29
landslide)) with GPS (global position system). The
study area is varying in relative topography and
lithology (Fig. 4), therefore distribution of landslide
occurrence is very different. Aerial photography can aid
to identify landslide especially with suitable scale (i.e.,
1:20000 or larger). Interpretation of aerial photography
and satellite images with view of stereoscopic
determined boundary of landslide which we cannot see
in the field work especially where topography is
hummocky or vegetation is dense (We determined 85
landslides in the entire area). Most of the landslides in
the area are more than 3000 m2, so we easily identify
them because aerial photograph stereoscopic with
1:20000 scale can accelerate for landslide finding in the
study area. These include old and new landslides which
most of old landslide need to control from ground for
accuracy in the landslide inventory preparing (Fig. 1).
Fig. 3: Landslide occurred above the village settlement
(Cheshme Kabud)
Factors related-landslide: According to the research
background, 8 parameters collected such as slope, slope
aspect, lithology, land use, erosion, distance to fault,
distance to river and distance to road. Geological paper
maps at 1:100, 000-scale covering the study area were
digitized and the geologic formations were identified.
The two largest datasets were topographical parameters
that were collected from the 1:25000-scale paper
topographic maps. A Digital Elevation Model (DEM)
was generated from a Triangulated Irregular Network
Fig. 1: Map of the study area and distribution of shallow
landslide (inventory) in the area that show on Ikonos
(above) and Geoeye (low) images (courtesy from
Geoeye company)
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Res. J. App. Sci. Eng. Technol., 7(15): 3174-3180, 2014
Fig. 4: Lithology map of Cheshme Kabud
Fig. 5: DEM and counter line of Cheshme Kabud map
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Res. J. App. Sci. Eng. Technol., 7(15): 3174-3180, 2014
Fig. 6: Briefly of the producing landslide susceptibility map in the study area
(TIN) model that was derived from digitized
topography contours with a contour interval of 25 m
(Fig. 5). The elevation, slope angle, aspect and shape of
the slope parameters were obtained from the DEM.
Another dataset was land use, which was
interpreted from ETM+ images on the 21 April 2009. It
was calibrated from using field observations. Because
of significant cloud coverage, results of the
classification were edited and simplified by manual
digitization. The images modify the boundaries by
supervision classification with ERDAS (Earth Resource
Data Analysis System) software. The accuracy of the
land use interpretation was checked in the field. Seven
main lands considered and classified. Based on
validation from field observations, the land use map has
the accuracy of the Landsat image spatial resolution
(~30 m). After geo-referencing the image, a
combination of bands 1, 4 and 7 was used to make
complex color images and operational information layer
created by the method of Categorization of Utmost
Probability (Dymond et al., 2006). Finally, zoning and
susceptibility of landslide were done through frequency
ratio method. Figure 6 shows the flowchart stages of
study.
RESULTS AND DISCUSSION
Instability is a phenomenon sometimes it is
observable and sometimes it is imperceptible. If the
latter occurs, only evidence of it can be searched. This
needs to be analysis of data and information. That is, to
analyze this phenomenon, it is necessary to fit the data
and information.
In principle, the only way to demonstrate the real
accuracy of the landslide evaluation maps when new
landslides occur after the generation of landslide
susceptibility. Spatial relationship between the factors
and the landslide susceptibility map was done using a
frequency ratio method. Weights and values the various
factors have been calculated for the classes is shown in
Table 1. Based on ratio it can be suggested that the
slope, lithology and landuse are the main role in the
occurrence of landslides in the area.
The relation between landslide distribution and
model was analyzed using Frequency Ratio (FR)
model. In the FR model, percentage of landslide
occurrence in a factor class divided by the total
percentage of that factor in the area Fig. 7.
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Res. J. App. Sci. Eng. Technol., 7(15): 3174-3180, 2014
Table 1: Frequency ratio calculation of landslide influencing factors
Factor
Slope
Aspect
Lithology
Landuse
Erosion
Road
Fault
River
Class
0-5%
6-10%
11-30%
31-60%
>60%
N
NE
E
SE
SE
SW
W
NW
Limestone
Young traces gravel fan
Radiolarian
As.spp-A.sc-Da.mu
As.spp-Fes.ov-Ho.bu
As.spp-po.bu
As.spp-po.bu-st.ba
CL
F
As.spp-Po.bu-An.gr
Low
Moderate
High
0-50
50-100
100-150
>150
0-1000 M
1000-2000 M
2000-4000 M
>5000
0-50
50-100
>100
Landslide point
3
27
38
15
2
16
25
10
10
5
3
7
9
24
34
27
6
7
1
15
17
29
10
15
37
33
42
24
15
4
37
20
18
10
47
22
16
Landslide (%)
3.50
31.80
44.70
17.70
2.30
18.80
29.40
11.80
11.80
5.90
3.50
8.20
10.60
28.20
40.00
31.80
7.10
8.20
1.20
17.60
20.00
34.10
11.80
17.60
43.60
38.80
49.50
28.20
17.60
4.70
43.52
23.52
21.18
11.78
55.30
25.88
18.82
Fig. 7: Landslide susceptibility map generated from frequency ratio method
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Number of pixel in
the class
453412
345673
287654
102876
78345
181216
126026
235057
135794
127425
103236
283795
75411
281487
754437
232036
195266
110312
40575
460270
51986
282755
126796
402133
574405
291422
568923
352678
100852
245507
392133
344340
231480
300007
492133
444343
331484
Class area
(%)
35.70
27.50
22.60
8.10
6.10
14.30
9.90
18.50
10.70
10.10
8.20
22.40
5.90
22.20
59.50
18.30
15.40
8.70
3.20
36.30
4.10
22.30
10.00
31.70
45.30
23.00
44.90
27.80
7.90
19.40
30.93
27.16
18.26
23.65
38.81
35.04
26.15
Ratio
0.10
1.16
1.98
2.19
0.38
1.31
1.10
0.64
1.10
0.58
0.43
0.37
1.80
1.27
0.67
1.73
0.46
0.94
0.62
0.48
4.87
1.52
1.18
0.55
0.96
1.68
1.10
1.01
2.23
0.24
1.40
0.86
1.16
0.50
1.42
0.74
0.72
Res. J. App. Sci. Eng. Technol., 7(15): 3174-3180, 2014
CONCLUSION
In this study, results of the susceptibility map have
been validated with the Receiver Operating
Characteristics (ROC) curve. Result of validation
represent more than 80% of prediction of the landslide
occurrence is suitable compare to past landslide
occurrence. The distribution of the landslide density
among different susceptibility classes is acceptability.
Landslides occurrence in the None-susceptible class is
the least while their density increases in the high-and
very high-susceptibility level.
ACKNOWLEDGMENT
Fig. 8: ROC graph for landslide susceptibility map validation
The FR value grater 1 represent probability of
landslide occurrence in this class is higher than average
of landslide occurrence in the area. Slope greater than
60° due to the lack of soil has small number of
landslides. Slope aspect is in degree (between 0 and
360°) from the north. It also defines the azimuth of the
flow. It was found that landslides in the study area in
the North West and North facing hillside (Table 1),
which are more abundant due to unexposed to the sun.
Fault and lineament map extracted directly of
geology map and it was controlled by Geoeye image
and band 7 of Landsat satellite image. There are not
large seismic faults in the study area, but it can be
divided into four regions based on the area density of
seismicity from high to low lineament density. The map
represent landslide points are more frequency at the
location where area is high seismicity. Also, by using
FR method, buffer zones between seismicity and
landslide occurrence assigning by buffer zones based
on various distance from lineaments. Up to the distance
of 2,000 m, the FR ratio indicated strong correlation
with landslide occurrence. It can be stated that near to
70% of landslide occurrence were around this area and
below the distance of 2,000 m, the ratio shows less
correlation, which is again an expected result.
The area under the ROC curve for each model is a
global statistical accuracy for each model. It is
independent of single prediction threshold which value
is varies between 0.5 and 1. If the curve has a more
distance from the reference line is better (Beguería,
2006). And, however, the closer the ROC curve is to
the upper left corner of the area is more accurate. If the
ROC is 1, it shows a perfect model and that the ROC is
equal to 0.5 indicates a random fit.
"Sensitivity" is probability of prediction of the
positive case which indicates, it is properly classified
and it is plotted on the y-axis in a ROC curve (Erener
et al., 2010). Based on their distance from the reference
line, the models show good results. From detection of
positive area (Sensitivity), FR model is more
performance. Area under curve for FR model was 0.93
(Fig. 8).
The authors wish to express their sincere thanks to
UTM university for providing a good platform to study
and also so many thanks to the Geoeye Company for
satellite images foundation.
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